A method to classify and identify microorganisms using the FTIR ATR technique.

The present disclosure relates to automated systems (200) and methods for quantitatively determining a fixation duration of a biological specimen using a trained fixation estimation engine (210). In some embodiments, the trained fixation estimation (210) engine includes a neural network. In some embodiments, the trained fixation estimation (210) engine includes a supervised classifier.

A calibration curve setting method used at the time of quantitatively analyzing specific components in a drug by a transmission Raman spectrum, the method comprising the steps of: obtaining respective transmission Raman spectra of a plurality of different wave number ranges including Raman hands corresponding to the specific components of a plurality of known drugs of which concentrations or amounts of the specific components are known and the concentrations or the amounts are different from each other; calculating candidate calibration curves which are candidates for calibration curves used for the quantitative analysis respectively from a plurality of transmission. Raman spectra in each of the wave number ranges; and using the most probable candidate calibration curve as a calibration curve for the quantitative analysis of the specific components, among the respective candidate calibration curves.

In some implementations, a device may receive spectroscopic data associated with a dynamic process. The device may generate a principal component analysis (PCA) model based on a first block of spectra from the spectroscopic data. The device may project a second block of spectra from the spectroscopic data to the PCA model generated based on the first block of spectra. The device may determine a value of a metric associated with the second block based on projecting the second block of spectra to the PCA model. The device may determine whether the dynamic process has reached an end point based on the value of the metric associated with the second block.

It is possible to more appropriately perform fluorescence separation. An information processing apparatus according to an embodiment includes: a fluorescence signal acquisition unit (112) that acquires a plurality of fluorescence spectra corresponding to each of a plurality of excitation lights having different wavelengths and irradiated to a fluorescence stained specimen (30), the fluorescence stained specimen (30) being created by staining a specimen (20) with a fluorescence reagent (10); a link unit (131) that generates a linked fluorescence spectrum by linking at least parts of the plurality of fluorescence spectra to each other in a wavelength direction; a separation unit (132) that separates the linked fluorescence spectrum into spectra for every fluorescent substance using a reference spectrum including a linked autofluorescence reference spectrum in which spectra of autofluorescent substances in the specimen are linked to each other in the wavelength direction and a linked fluorescence reference spectrum in which spectra of fluorescent substances in the fluorescence stained specimen are linked to each other in the wavelength direction; and an extraction unit (132) that updates the linked autofluorescence reference spectrum using the spectra for every fluorescent substance separated by the separation unit.

A method for improving an identification accuracy of mixture components by using a known mixture Raman spectrum is disclosed. After calculating a first similarity between a to-be-tested Raman spectrum characteristic vector group and a pure substance Raman spectrum characteristic vector group of an nth kind of pure substance in a Raman spectrum standard library, the method uses a known mixture library to calculate to obtain a second similarity between a to-be-identified substance Raman spectrum characteristic vector group and a spectral peak characteristic vector group with offset information corresponding to a pure substance in a known mixture, and determines a similarity between a to-be-tested mixture and the nth kind of pure substance according to the first similarity and all second similarities to thus obtain a component identification result. The present application uses the known mixture library to assist the Raman spectrum standard library in searching.

Systems, devices, and methods including one or more optical cavities; one or more light sources configured to emit a specified wavelength or band of wavelengths of light; and one or more photovoltaic detectors configured to receive the emitted light that has traveled over one or more path lengths, where the one or more photovoltaic detectors are configured to detect at least one of: a first trace gas species and a second trace gas species.

The use of Raman spectroscopy for the monitoring and assessment of viral titre is disclosed. A method of quantifying viral titre in a sample using Raman spectroscopy, comprises the steps of: (a) providing a sample and irradiating the sample with a light source; (b) measuring the total intensity of Raman scattered light within each one of a plurality of wavenumber ranges to obtain a wavenumber intensity data set for the sample, wherein the plurality of wavenumber ranges are pre-selected and are characteristic of the vims in the sample; (c) performing mathematical data processing steps on the wavenumber intensity data; and (d) quantifying the viral titre based upon the output of the mathematical data processing steps.

The present invention relates generally to a slope spectroscopy standards and methods of making slope spectroscopy standards, specifically standards and methods of developing standards specifically for variable pathlength (slope) measurements.

A bloodless system for numerically generated hyperspectral imaging data for measuring biochemical compositions is disclosed which includes an optical imaging device adapted to acquire an RGB image from an area of interest, a processor adapted to receive a hyperspectral dataset representing an a priori hyperspectral data of the area of interest of a population to which the subject belongs, receive RGB response for each one of RGB channels of the optical imaging device, pair the corresponding RGB data with the hyperspectral data, obtain a transformation matrix adapted to convert a subject-specific RGB image dataset into a subject-specific hyperspectral dataset for the optical imaging device, receive a subject- specific RGB dataset, generate a subject- specific hyperspectral dataset using the transformation matrix, and compute a blood hemoglobin level of the subject from the generated subject-specific hyperspectral dataset.